MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy.
MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy.
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DOI：
10.1093/nargab/lqaa028
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发表时间：
2020-06
影响因子：
4.6
通讯作者：
Liu J
中科院分区：
文献类型：
--
作者：
Cheng Q;Yang Y;Shi X;Yeung KF;Yang C;Peng H;Liu J
全基因组关联研究（GWAS）的激增促使人们使用双样本孟德尔随机化（MR），以遗传变异作为工具变量（IV）来绘制健康风险因素和疾病结果之间可靠的因果关系。然而，GWAS 的独特特征要求 MR 方法同时考虑连锁不平衡 (LD) 和复杂性状中普遍存在的水平多效性，这是一种变异通过机制影响结果的现象，而不仅仅是通过暴露。因此，未能考虑 LD 和水平多效性的统计方法可能会导致估计偏差和假阳性因果关系。为了克服这些局限性，我们提出了一种 MR 分析的概率模型，在 LD 存在的情况下使用 GWAS 摘要统计来识别风险因素和疾病结果之间的因果效应，并正确考虑遗传变异之间的水平多效性 (MR-LDP)，并开发一种计算有效的算法来进行因果推断。然后，我们进行了全面的模拟研究，以证明 MR-LDP 相对于现有方法的优势。此外，我们使用两个真实的暴露-结果对来验证 MR-LDP 与其他方法相比的结果，表明我们的方法在 LD 中使用全仪器变体时更有效。通过进一步将 MR-LDP 应用于作为复杂疾病危险因素的脂质特征和体重指数 (BMI)，我们确定了多对显着因果关系，包括高密度脂蛋白胆固醇对周围血管疾病的保护作用以及 BMI 对痔疮的积极因果作用。
The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure–outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.
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